Fast feature selection algorithm for neighborhood rough set model based on Bucket and Trie structures

Benouini, Rachid; Batioua, Imad; Ezghari, Soufiane; Zenkouar, Khalid; Zahi, Azeddine

doi:10.1007/s41066-019-00162-w

Cited by 10 publications

(3 citation statements)

References 55 publications

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“…The main purpose of this process is to reduce the training time and amount of memory required for the algorithm to work, thus reducing the computational cost when developing a predictive model (Zebari et al, 2020). In some cases, it also improves the performance of the model, although this is not always guaranteed (Benouini et al, 2020).…”

Section: Feature Selectionmentioning

confidence: 99%

Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials

Febles

Ortega

Sosa

et al. 2022

Front. Neuroinform.

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AntecedentThe event-related potential (ERP) components P300 and mismatch negativity (MMN) have been linked to cognitive deficits in patients with schizophrenia. The diagnosis of schizophrenia could be improved by applying machine learning procedures to these objective neurophysiological biomarkers. Several studies have attempted to achieve this goal, but no study has examined Multiple Kernel Learning (MKL) classifiers. This algorithm finds optimally a combination of kernel functions, integrating them in a meaningful manner, and thus could improve diagnosis.ObjectiveThis study aimed to examine the efficacy of the MKL classifier and the Boruta feature selection method for schizophrenia patients (SZ) and healthy controls (HC) single-subject classification.MethodsA cohort of 54 SZ and 54 HC participants were studied. Three sets of features related to ERP signals were calculated as follows: peak related features, peak to peak related features, and signal related features. The Boruta algorithm was used to evaluate the impact of feature selection on classification performance. An MKL algorithm was applied to address schizophrenia detection.ResultsA classification accuracy of 83% using the whole dataset, and 86% after applying Boruta feature selection was obtained. The variables that contributed most to the classification were mainly related to the latency and amplitude of the auditory P300 paradigm.ConclusionThis study showed that MKL can be useful in distinguishing between schizophrenic patients and controls when using ERP measures. Moreover, the use of the Boruta algorithm provides an improvement in classification accuracy and computational cost.

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Section: Feature Selectionmentioning

confidence: 99%

Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials

Febles

Ortega

Sosa

et al. 2022

Front. Neuroinform.

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“…Therefore, it allows us to reduce the number of input variables. The goal of this process is to reduce the computational cost when developing a predictive model and, in some cases, to improve the performance of the model, not always guarantee (Benouini et al, 2020).…”

Section: Feature Selectionmentioning

confidence: 99%

Machine Learning techniques for the diagnosis of Schizophrenia based on Event Related Potentials

Febles

Ortega

Valdés

et al. 2022

Preprint

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Antecedent: The diagnosis of schizophrenia could be enhanced with objective neurophysiological biomarkers, such as the event related potential features in conjunction with machine learning procedures. A previous work extracted features from event related responses to three oddball paradigms (auditory and visual P300, and mismatch negativity) for the discrimination of schizophrenic patients. They used several classifiers: Naive Bayes, Support Vector Machine, Decision Tree, Adaboost and Random Forest. The best accuracy was obtained with Random Forest (84.7%). Objective: The aim of this study was to examine the efficacy of Multiple Kernel Learning classifiers and Boruta feature selection method exploring different features for single-subject classification between schizophrenia patients and healthy controls. Methods: A cohort of 54 schizophrenic subjects and 54 healthy control subjects were studied. Three sets of features related to the event related potentials signal were calculated: Peak related features, Peak to Peak related features and Signal related features. The Boruta feature selection algorithm was used to evaluate its impact on classification accuracy. A Multiple Kernel Learning algorithm was applied to address schizophrenia detection. Results: We obtained a classification accuracy of 83% using Multiple Kernel Learning classifier with the whole dataset. This result in accuracy triangulates previous work and shows that the differences between schizophrenic patients and controls are robust even when different classifiers are used. Appling the Boruta feature selection algorithm a classification accuracy of 86% was yielded. The variables that contributed most to the classification were mainly related to the latency and amplitude of the auditory P300. Conclusion: This study showed that Multiple Kernel Learning can be useful in distinguishing between schizophrenic patients and controls. Moreover, the combination with the Boruta algorithm provides an improvement in classification accuracy and computational cost.

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“…Many proposals made for generalizing and interpreting rough sets (Orlowska and Pawlak 1984;Pomykala 1987;Skowron and Stepaniuk 1996;Yao and Line 1996;Zirako 1994). Some applicable examples of real-life fields of the rough set method can be cited such as in Process Control, Economics, Medical Diagnosis, Biochemistry, Environmental Science, Biology, Chemistry, Psychology, Conflict Analysis, Pharmacology, Banking, Market Research, Engineering, Speech Recognition, Material Science, Information Analysis, Data Analysis, Data Mining, Control and Linguistics and many other fields [See, (Allam et al 2005;Benouini et al 2020;Dong et al 2004;Jensen and Shen 2004;Leung et al 2006;Pal and Mitra 2004;Yao and Chen 2005;Zhao and Liu 2011;Zhan et al 2019)]. In 1999, Yao (1999) introduced generalized rough sets through a binary relation; while, these approximations are not satisfied with Pawlak's properties that were applied on an equivalence relation.…”

Section: Introductionmentioning

confidence: 99%

Rough approximation models via graphs based on neighborhood systems

2020

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Neighborhood systems are used to approximate graphs as finite topological structures. Throughout this article, we construct new types of eight neighborhoods for vertices of an arbitrary graph, say, j-adhesion neighborhoods. Both notions of Allam et al. and Yao will be extended via j-adhesion neighborhoods. We investigate new types of j-lower approximations and jupper approximations for any subgraph of a given graph. Then, the accuracy of these approximations will be calculated. Moreover, a comparison between accuracy measures and boundary regions for different kinds of approximations will be discussed. To generate j-adhesion neighborhoods and rough sets on graphs, some algorithms will be introduced. Finally, a sample of a chemical example for Walczak will be introduced to illustrate our proposed methods.

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Fast feature selection algorithm for neighborhood rough set model based on Bucket and Trie structures

Cited by 10 publications

References 55 publications

Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials

Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials

Machine Learning techniques for the diagnosis of Schizophrenia based on Event Related Potentials

Rough approximation models via graphs based on neighborhood systems

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